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There is no mandatory textbook for the course. We will provide lecture notes, or reading from books. Some good books include: The Design and Analysis of Algorithms by Dexter Kozen: CMU Access via SpringerLink.
Algorithm Design by Kleinberg and Tardos: CMU library, slides by Kevin Wayne.
Algorithms by Dasgupta, Papadimitriou, and Vazirani (DPV): CMU library, author's site.
Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (CLRS): CMU library with links to e-copy.
Randomized Algorithms by Motwani and Raghavan: CMU Access to e-copy.
Algorithms by Jeff Erickson: online PDF.
Design and Analysis of Computer Algorithms by Aho, Hopcroft, and Ullman: CMU library.
We assume basic discrete mathematics (counting, basic probability, basic graphs theory, basic linear algebra): some resources include: (15-251) Great Theoretical Ideas in CS: slides from our undergraduate course.
(15-151) Discrete Mathematics: our undergraduate course page (contains the textbook).
Mathematics for Computer Science by Lehman, Leighton, and Meyer: lecture notes from MIT.
A useful stackexchange thread on good linear algebra sources (with links to several free texts).
A primer on matrices (by Shephen Boyd), and a linear algebra review (from Stanford's cs229, by our own Zico Kolter) with some multivariate calculus too.
Some helpful videos on linear algebra (thanks Anil!).
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